光学学报, 2018, 38 (11): 1115002, 网络出版: 2019-05-09   

基于特征融合的长时目标跟踪算法 下载: 1146次

Long-Term Object Tracking Based On Feature Fusion
作者单位
陆军工程大学无人机工程系, 河北 石家庄 050003
摘要
针对长时目标跟踪中目标遮挡、目标出视野等因素导致的目标失跟问题,提出一种基于特征融合的长时目标跟踪算法,提高目标跟踪的速度和稳健性。首先,融合目标方向梯度直方图特征、颜色空间特征和局部敏感直方图特征,来增强算法在复杂情况下的特征判别力,提高目标跟踪的稳健性,并对融合特征进行降维来提高目标跟踪的速度;然后,通过额外的一维尺度相关滤波器来获得目标最优的尺度估计,并通过正交三角分解来无损降低计算复杂度;最后,自适应确定目标检测阈值,在目标遮挡或出视野导致目标失跟时,通过EdgeBoxes方法提取目标候选区域,利用结构化支持向量机重新检测目标位置达到长时跟踪的目的。在标准跟踪数据集OTB2015和UAV123上进行实验。结果表明,本文算法较对比算法中最优算法目标跟踪平均精度提升5.0%,目标跟踪平均成功率提升2.6%,目标跟踪平均速度为28.2 frame/s,可满足跟踪的实时性要求。在目标受到遮挡、出视野等情况下,该算法仍能够对目标进行持续准确的跟踪。
Abstract
Aim

ing at the problems of object tracking failure caused by occlusion and out of view in long-term tracking, we propose a long-term object tracking algorithm based on feature fusion to improve the speed and robustness of object tracking. First, the features of histogram of oriented gradient, color space and local sensitive histogram are fused to enhance the robustness of the algorithm in complex cases, and the fusion feature dimension reduction is carried out to improve the object tracking speed. Then, an additional one-dimensional scale correlation filter is used to obtain the optimal scale estimation of the object, and the computational complexity is reduced by quadrature rectangle-factorization. Finally, the object detection threshold is adaptively determined. When the object occlusion or out-of-view causes the failure of object tracking, the object region proposals can be extracted by EdgeBoxes, and object position is re-directed by using structured support vector machine to complete the long-term tracking of object. Experiments are conducted on standard tracking datasets OTB2015 and UAV123. Experimental results show that the average accuracy of the proposed algorithm is 5.0% higher than that of other optimal algorithms, the average success rate is increased by 2.6%, and the average object tracking speed is 28.2 frame/s, which meets the real-time requirements for tracking. In the case of object occlusion and out of view, the proposed algorithm can track the object continuously and accurately.

葛宝义, 左宪章, 胡永江. 基于特征融合的长时目标跟踪算法[J]. 光学学报, 2018, 38(11): 1115002. Baoyi Ge, Xianzhang Zuo, Yongjiang Hu. Long-Term Object Tracking Based On Feature Fusion[J]. Acta Optica Sinica, 2018, 38(11): 1115002.

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